U.S. House prices
Abstract/Contents
- Abstract
- In this thesis, I develop a deep learning model for predicting house prices in the U.S. House price prediction has been increasingly important to many sectors of the economy, such as real estate, mortgage investment and risk management, and government property tax collection. Among all the factors, foreclosure is believed to have a huge impact on house prices. When the real estate bubble burst after the 2008 subprime crisis, there has been a significant portion of sales being foreclosed. This motivates researchers to investigate how foreclosure impacts house prices and by which channels it happens. Here I use an unprecedented dataset provided by the data vendor CoreLogic of over 100 million housing transactions across the U.S. from 2004 to 2014. Within my deep learning framework, I handle missing data through an efficient imputation method, address spatial and temporal effects through new features created, and finally achieve a superior out-of-sample predictive performance on datasets across the country. With the fitted model, I further explore the relationship between foreclosure discount and other variables such as the underlying house price, the age of the house and the neighborhood house price, which couldn't be fully characterized by previous linear models. I find significant regional differences as well as universal patterns across different areas.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Wei, Yexiang | |
|---|---|---|
| Associated with | Stanford University, Department of Management Science and Engineering. | |
| Primary advisor | Giesecke, Kay | |
| Thesis advisor | Giesecke, Kay | |
| Thesis advisor | Lai, T. L | |
| Thesis advisor | Pelger, Markus | |
| Advisor | Lai, T. L | |
| Advisor | Pelger, Markus |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Yexiang Wei. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Yexiang Wei
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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